{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-10T12:11:35.172757Z",
     "start_time": "2018-05-10T12:11:34.608349Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-10T12:11:35.186767Z",
     "start_time": "2018-05-10T12:11:35.173758Z"
    }
   },
   "outputs": [],
   "source": [
    "gmf_hr = pd.read_csv('./data/gmf_HR.csv')\n",
    "mlp_hr = pd.read_csv('./data/mlp_HR.csv')\n",
    "mlp_pretrain_hr = pd.read_csv('./data/mlp_pretrain_HR.csv')\n",
    "\n",
    "gmf_ndcg = pd.read_csv('./data/gmf_NDCG.csv')\n",
    "mlp_ndcg = pd.read_csv('./data/mlp_NDCG.csv')\n",
    "mlp_pretrain_ndcg = pd.read_csv('./data/mlp_pretrain_NDCG.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-10T12:11:35.426941Z",
     "start_time": "2018-05-10T12:11:35.187768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TP6Q/G3I2EOUXRa+AXi0F3Sbp5eksSVtCRkUGg8MGY3faSS9P51DZIQ6VH6LaXs2s/rMY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\nRERCoQJIRERCoQJIRERC8f8BPN3U8AB6CwsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c8a039eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(gmf_hr['Step'], gmf_hr['Value'], label='GMF-HR')\n",
    "ax.plot(mlp_hr['Step'], mlp_hr['Value'], label='MLP-HR')\n",
    "ax.plot(mlp_pretrain_hr['Step'], mlp_pretrain_hr['Value'], label='MLP-Pretrain-HR')\n",
    "\n",
    "ax.set(xlabel='Epoch', ylabel='Hit Ratio@10',\n",
    "       title='Hit Ratio: MLP performs slightly better than GMF after pretraining')\n",
    "ax.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-10T12:11:35.637093Z",
     "start_time": "2018-05-10T12:11:35.428943Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YdCC9Qxp3GuhIetozq3I4ybVVcWjXFuacU19ZF5ZXYzYJAr3bZhruSNyfV15p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\nC9KzlmdNlBE5/5y1WLanPI3SA8gDdgB9gbbAeuCCHMXSCxg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c8a532208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(gmf_ndcg['Step'], gmf_ndcg['Value'], label='GMF-HR')\n",
    "ax.plot(mlp_ndcg['Step'], mlp_ndcg['Value'], label='MLP-HR')\n",
    "ax.plot(mlp_pretrain_ndcg['Step'], mlp_pretrain_ndcg['Value'], label='MLP-Pretrain-HR')\n",
    "\n",
    "ax.set(xlabel='Epoch', ylabel='NDCG@10',\n",
    "       title='NDCG: MLP performs slightly better than GMF after pretraining')\n",
    "ax.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
